





from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          LayoutLMvaConfig,
          TFLayoutLMvaForQuestionAnswering,
          TFLayoutLMvaForSequenceClassification,
          TFLayoutLMvaForTokenClassification,
          TFLayoutLMvaModel,
      )

if is_vision_available():
      from PIL import Image

      from transformers import LayoutLMvaImageProcessor





class 					A_					:







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						Optional[int]    ,__A						:						str=2    ,__A						:						List[str]=3    ,__A						:						List[Any]=4    ,__A						:						List[Any]=2    ,__A						:						List[str]=7    ,__A						:						Dict=True    ,__A						:						Union[str, Any]=True    ,__A						:						Optional[Any]=True    ,__A						:						Optional[Any]=True    ,__A						:						int=99    ,__A						:						str=36    ,__A						:						Tuple=2    ,__A						:						int=4    ,__A						:						int=37    ,__A						:						Union[str, Any]="gelu"    ,__A						:						str=0.1    ,__A						:						Union[str, Any]=0.1    ,__A						:						Any=512    ,__A						:						Optional[int]=16    ,__A						:						List[Any]=2    ,__A						:						int=0.02    ,__A						:						Union[str, Any]=6    ,__A						:						Dict=6    ,__A						:						str=3    ,__A						:						Optional[Any]=4    ,__A						:						str=None    ,__A						:						str=1000    ,)    ->       Dict:
         _lowercase						    =			parent
         _lowercase						    =			batch_size
         _lowercase						    =			num_channels
         _lowercase						    =			image_size
         _lowercase						    =			patch_size
         _lowercase						    =			is_training
         _lowercase						    =			use_input_mask
         _lowercase						    =			use_token_type_ids
         _lowercase						    =			use_labels
         _lowercase						    =			vocab_size
         _lowercase						    =			hidden_size
         _lowercase						    =			num_hidden_layers
         _lowercase						    =			num_attention_heads
         _lowercase						    =			intermediate_size
         _lowercase						    =			hidden_act
         _lowercase						    =			hidden_dropout_prob
         _lowercase						    =			attention_probs_dropout_prob
         _lowercase						    =			max_position_embeddings
         _lowercase						    =			type_vocab_size
         _lowercase						    =			type_sequence_label_size
         _lowercase						    =			initializer_range
         _lowercase						    =			coordinate_size
         _lowercase						    =			shape_size
         _lowercase						    =			num_labels
         _lowercase						    =			num_choices
         _lowercase						    =			scope
         _lowercase						    =			range_bbox

         # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
         _lowercase						    =			text_seq_length
         _lowercase						    =			(image_size // patch_size) ** 2 + 1
         _lowercase						    =			self.text_seq_length + self.image_seq_length







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Optional[Any]:
         _lowercase						    =			ids_tensor([self.batch_size, self.text_seq_length]    ,self.vocab_size	)

         _lowercase						    =			ids_tensor([self.batch_size, self.text_seq_length, 4]    ,self.range_bbox	)
         _lowercase						    =			bbox.numpy()
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]	):
           for j in range(bbox.shape[1]	):
             if bbox[i, j, 3] < bbox[i, j, 1]:
               _lowercase						    =			bbox[i, j, 3]
               _lowercase						    =			bbox[i, j, 1]
               _lowercase						    =			tmp_coordinate
             if bbox[i, j, 2] < bbox[i, j, 0]:
               _lowercase						    =			bbox[i, j, 2]
               _lowercase						    =			bbox[i, j, 0]
               _lowercase						    =			tmp_coordinate
         _lowercase						    =			tf.constant(__A	)

         _lowercase						    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         _lowercase						    =			None
         if self.use_input_mask:
           _lowercase						    =			random_attention_mask([self.batch_size, self.text_seq_length]	)

         _lowercase						    =			None
         if self.use_token_type_ids:
           _lowercase						    =			ids_tensor([self.batch_size, self.text_seq_length]    ,self.type_vocab_size	)

         _lowercase						    =			None
         _lowercase						    =			None
         if self.use_labels:
           _lowercase						    =			ids_tensor([self.batch_size]    ,self.type_sequence_label_size	)
           _lowercase						    =			ids_tensor([self.batch_size, self.text_seq_length]    ,self.num_labels	)

         _lowercase						    =			LayoutLMvaConfig(
             vocab_size=self.vocab_size    ,hidden_size=self.hidden_size    ,num_hidden_layers=self.num_hidden_layers    ,num_attention_heads=self.num_attention_heads    ,intermediate_size=self.intermediate_size    ,hidden_act=self.hidden_act    ,hidden_dropout_prob=self.hidden_dropout_prob    ,attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,max_position_embeddings=self.max_position_embeddings    ,type_vocab_size=self.type_vocab_size    ,initializer_range=self.initializer_range    ,coordinate_size=self.coordinate_size    ,shape_size=self.shape_size    ,input_size=self.image_size    ,patch_size=self.patch_size    ,)

         return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







       def  __UpperCAmelCase			(     self						:						Optional[int]    ,__A						:						List[Any]    ,__A						:						int    ,__A						:						List[Any]    ,__A						:						Tuple    ,__A						:						Optional[Any]    ,__A						:						List[Any]	)    ->       Optional[Any]:
         _lowercase						    =			TFLayoutLMvaModel(config=__A	)

         # text + image
         _lowercase						    =			model(__A    ,pixel_values=__A    ,training=__A	)
         _lowercase						    =			model(
             __A    ,bbox=__A    ,pixel_values=__A    ,attention_mask=__A    ,token_type_ids=__A    ,training=__A    ,)
         _lowercase						    =			model(__A    ,bbox=__A    ,pixel_values=__A    ,training=__A	)

         self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)	)

         # text only
         _lowercase						    =			model(__A    ,training=__A	)
         self.parent.assertEqual(
             result.last_hidden_state.shape    ,(self.batch_size, self.text_seq_length, self.hidden_size)	)

         # image only
         _lowercase						    =			model({'pixel_values': pixel_values}    ,training=__A	)
         self.parent.assertEqual(
             result.last_hidden_state.shape    ,(self.batch_size, self.image_seq_length, self.hidden_size)	)







       def  __UpperCAmelCase			(     self						:						int    ,__A						:						Optional[Any]    ,__A						:						List[str]    ,__A						:						List[str]    ,__A						:						List[Any]    ,__A						:						int    ,__A						:						Union[str, Any]    ,__A						:						List[str]	)    ->       Tuple:
         _lowercase						    =			self.num_labels
         _lowercase						    =			TFLayoutLMvaForSequenceClassification(config=__A	)
         _lowercase						    =			model(
             __A    ,bbox=__A    ,pixel_values=__A    ,attention_mask=__A    ,token_type_ids=__A    ,labels=__A    ,training=__A    ,)
         self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)	)







       def  __UpperCAmelCase			(     self						:						Any    ,__A						:						Any    ,__A						:						Optional[Any]    ,__A						:						Optional[int]    ,__A						:						Dict    ,__A						:						List[str]    ,__A						:						Tuple    ,__A						:						Optional[Any]	)    ->       Optional[Any]:
         _lowercase						    =			self.num_labels
         _lowercase						    =			TFLayoutLMvaForTokenClassification(config=__A	)
         _lowercase						    =			model(
             __A    ,bbox=__A    ,pixel_values=__A    ,attention_mask=__A    ,token_type_ids=__A    ,labels=__A    ,training=__A    ,)
         self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.text_seq_length, self.num_labels)	)







       def  __UpperCAmelCase			(     self						:						int    ,__A						:						Optional[Any]    ,__A						:						Union[str, Any]    ,__A						:						int    ,__A						:						List[Any]    ,__A						:						str    ,__A						:						Optional[Any]    ,__A						:						Any	)    ->       Optional[Any]:
         _lowercase						    =			2
         _lowercase						    =			TFLayoutLMvaForQuestionAnswering(config=__A	)
         _lowercase						    =			model(
             __A    ,bbox=__A    ,pixel_values=__A    ,attention_mask=__A    ,token_type_ids=__A    ,start_positions=__A    ,end_positions=__A    ,training=__A    ,)
         self.parent.assertEqual(result.start_logits.shape    ,(self.batch_size, self.seq_length)	)
         self.parent.assertEqual(result.end_logits.shape    ,(self.batch_size, self.seq_length)	)







       def  __UpperCAmelCase			(     self						:						int	)    ->       str:
         _lowercase						    =			self.prepare_config_and_inputs()
         ((_lowercase)    ,		(_lowercase)    ,		(_lowercase)    ,		(_lowercase)    ,		(_lowercase)    ,		(_lowercase)    ,		(_lowercase)    ,		(_lowercase))						    =			config_and_inputs
         _lowercase						    =			{
             'input_ids': input_ids,
             'bbox': bbox,
             'pixel_values': pixel_values,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict





@require_tf
class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Any  			=	(
           (
               TFLayoutLMvaModel,
               TFLayoutLMvaForQuestionAnswering,
               TFLayoutLMvaForSequenceClassification,
               TFLayoutLMvaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_   :			Dict  			=	(
           {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
           if is_tf_available()
           else {}
       )

       SCREAMING_SNAKE_CASE_   :			int  			=	False
       SCREAMING_SNAKE_CASE_   :			Optional[int]  			=	False
       SCREAMING_SNAKE_CASE_   :			Optional[int]  			=	False







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						str    ,__A						:						Any    ,__A						:						Optional[int]    ,__A						:						Tuple    ,__A						:						str	)    ->       Any:
         return True







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Union[str, Any]    ,__A						:						Tuple    ,__A						:						Optional[Any]=False	)    ->       dict:
         _lowercase						    =			copy.deepcopy(__A	)

         if model_class in get_values(__A	):
           _lowercase						    =			{
               k: tf.tile(tf.expand_dims(__A    ,1	)    ,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)	)
               if isinstance(__A    ,tf.Tensor	) and v.ndim > 0
               else v
               for k, v in inputs_dict.items()
           }

         if return_labels:
           if model_class in get_values(__A	):
             _lowercase						    =			tf.ones(self.model_tester.batch_size    ,dtype=tf.intaa	)
           elif model_class in get_values(__A	):
             _lowercase						    =			tf.zeros(self.model_tester.batch_size    ,dtype=tf.intaa	)
             _lowercase						    =			tf.zeros(self.model_tester.batch_size    ,dtype=tf.intaa	)
           elif model_class in get_values(__A	):
             _lowercase						    =			tf.zeros(self.model_tester.batch_size    ,dtype=tf.intaa	)
           elif model_class in get_values(__A	):
             _lowercase						    =			tf.zeros(
                 (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,dtype=tf.intaa	)

         return inputs_dict







       def  __UpperCAmelCase			(     self						:						int	)    ->       Tuple:
         _lowercase						    =			TFLayoutLMvaModelTester(self	)
         _lowercase						    =			ConfigTester(self    ,config_class=__A    ,hidden_size=37	)







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       Any:
         self.config_tester.run_common_tests()







       def  __UpperCAmelCase			(     self						:						Any	)    ->       List[str]:
         _lowercase    ,		_lowercase						    =			self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           _lowercase						    =			model_class(__A	)
           if getattr(__A    ,'hf_compute_loss'    ,__A	):
             # The number of elements in the loss should be the same as the number of elements in the label
             _lowercase						    =			self._prepare_for_class(inputs_dict.copy()    ,__A    ,return_labels=__A	)
             _lowercase						    =			prepared_for_class[
                 sorted(prepared_for_class.keys() - inputs_dict.keys()    ,reverse=__A	)[0]
             ]
             _lowercase						    =			added_label.shape.as_list()[:1]

             # Test that model correctly compute the loss with kwargs
             _lowercase						    =			self._prepare_for_class(inputs_dict.copy()    ,__A    ,return_labels=__A	)
             _lowercase						    =			prepared_for_class.pop('input_ids'	)

             _lowercase						    =			model(__A    ,**__A	)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

             # Test that model correctly compute the loss when we mask some positions
             _lowercase						    =			self._prepare_for_class(inputs_dict.copy()    ,__A    ,return_labels=__A	)
             _lowercase						    =			prepared_for_class.pop('input_ids'	)
             if "labels" in prepared_for_class:
               _lowercase						    =			prepared_for_class['labels'].numpy()
               if len(labels.shape	) > 1 and labels.shape[1] != 1:
                 _lowercase						    =			-100
                 _lowercase						    =			tf.convert_to_tensor(__A	)
                 _lowercase						    =			model(__A    ,**__A	)[0]
                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)
                 self.assertTrue(not np.any(np.isnan(loss.numpy()	)	)	)

                # Test that model correctly compute the loss with a dict
             _lowercase						    =			self._prepare_for_class(inputs_dict.copy()    ,__A    ,return_labels=__A	)
             _lowercase						    =			model(__A	)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

             # Test that model correctly compute the loss with a tuple
             _lowercase						    =			self._prepare_for_class(inputs_dict.copy()    ,__A    ,return_labels=__A	)

             # Get keys that were added with the _prepare_for_class function
             _lowercase						    =			prepared_for_class.keys() - inputs_dict.keys()
             _lowercase						    =			inspect.signature(model.call	).parameters
             _lowercase						    =			list(signature.keys()	)

             # Create a dictionary holding the location of the tensors in the tuple
             _lowercase						    =			{0: 'input_ids'}
             for label_key in label_keys:
               _lowercase						    =			signature_names.index(__A	)
               _lowercase						    =			label_key
             _lowercase						    =			sorted(tuple_index_mapping.items()	)
             # Initialize a list with their default values, update the values and convert to a tuple
             _lowercase						    =			[]

             for name in signature_names:
               if name != "kwargs":
                 list_input.append(signature[name].default	)

             for index, value in sorted_tuple_index_mapping:
               _lowercase						    =			prepared_for_class[value]

             _lowercase						    =			tuple(__A	)

             # Send to model
             _lowercase						    =			model(tuple_input[:-1]	)[0]

             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Optional[Any]:
         (
             (
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		
         )						    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(__A    ,__A    ,__A    ,__A    ,__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       int:
         (
             (
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		
         )						    =			self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
           _lowercase						    =			type
           self.model_tester.create_and_check_model(__A    ,__A    ,__A    ,__A    ,__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Union[str, Any]:
         (
             (
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		
         )						    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(
             __A    ,__A    ,__A    ,__A    ,__A    ,__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						Dict	)    ->       Dict:
         (
             (
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		
         )						    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(
             __A    ,__A    ,__A    ,__A    ,__A    ,__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Optional[int]:
         (
             (
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		(
             _lowercase
         )    ,		
         )						    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(
             __A    ,__A    ,__A    ,__A    ,__A    ,__A    ,__A	)







       @slow
       def  __UpperCAmelCase			(     self						:						Dict	)    ->       Dict:
         for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase						    =			TFLayoutLMvaModel.from_pretrained(__A	)
           self.assertIsNotNone(__A	)

def 				SCREAMING_SNAKE_CASE__							(	)   ->    Optional[Any]:
  _lowercase						    =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image







@require_tf
class 					A_					(   unittest.TestCase						):







       """simple docstring"""





       @cached_property
       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       Optional[Any]:
         return LayoutLMvaImageProcessor(apply_ocr=__A	) if is_vision_available() else None







       @slow
       def  __UpperCAmelCase			(     self						:						int	)    ->       Any:
         _lowercase						    =			TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	)

         _lowercase						    =			self.default_image_processor
         _lowercase						    =			prepare_img()
         _lowercase						    =			image_processor(images=__A    ,return_tensors='tf'	).pixel_values

         _lowercase						    =			tf.constant([[1, 2]]	)
         _lowercase						    =			tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]	)    ,axis=0	)

         # forward pass
         _lowercase						    =			model(input_ids=__A    ,bbox=__A    ,pixel_values=__A    ,training=__A	)

         # verify the logits
         _lowercase						    =			(1, 199, 768)
         self.assertEqual(outputs.last_hidden_state.shape    ,__A	)

         _lowercase						    =			tf.constant(
             [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	)

         self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    ,__A    ,atol=1e-4	)	)






'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCAmelCase									=   logging.get_logger(__name__)
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_		,   **a_						):
			lowerCAmelCase  :     Tuple       						= feature_size
			lowerCAmelCase  :     int       						= sampling_rate
			lowerCAmelCase  :     str       						= padding_value

			lowerCAmelCase  :     int       						= kwargs.pop("padding_side"		,   "right"						)
			lowerCAmelCase  :     int       						= kwargs.pop("return_attention_mask"		,   a_						)

			super().__init__(**a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = True		,   a_ = None		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			# If we have a list of dicts, let's convert it in a dict of lists
			# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
			if isinstance(a_		,   (list, tuple)						) and isinstance(processed_features[0]		,   (dict, BatchFeature)						):
					lowerCAmelCase  :     str       						= {
					    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
					}

			# The model's main input name, usually `input_values`, has be passed for padding
			if self.model_input_names[0] not in processed_features:
					raise ValueError(
					    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
					    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
					    F''' {list(processed_features.keys()						)}'''						)

			lowerCAmelCase  :     Any       						= processed_features[self.model_input_names[0]]
			lowerCAmelCase  :     Dict       						= (
			    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
			)

			if len(a_						) == 0:
					if return_attention_mask:
							lowerCAmelCase  :     List[Any]       						= []
					return processed_features

			# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
			# and rebuild them afterwards if no return_tensors is specified
			# Note that we lose the specific device the tensor may be on for PyTorch

			lowerCAmelCase  :     Optional[int]       						= required_input[0]
			if isinstance(a_		,   (list, tuple)						):
					# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
					lowerCAmelCase  :     Optional[Any]       						= 0
					while len(required_input[index]						) == 0:
							index += 1
					if index < len(a_						):
							lowerCAmelCase  :     Union[str, Any]       						= required_input[index][0]

			if return_tensors is None:
					if is_tf_tensor(a_						):
							lowerCAmelCase  :     Tuple       						= "tf"
					elif is_torch_tensor(a_						):
							lowerCAmelCase  :     str       						= "pt"
					elif isinstance(a_		,   (int, float, list, tuple, np.ndarray)						):
							lowerCAmelCase  :     Dict       						= "np"
					else:
							raise ValueError(
							    F'''type of {first_element} unknown: {type(a_						)}. '''
							    "Should be one of a python, numpy, pytorch or tensorflow object."						)

			for key, value in processed_features.items():
					if isinstance(value[0]		,   (int, float)						):
							lowerCAmelCase  :     Dict       						= to_numpy(a_						)
					else:
							lowerCAmelCase  :     Any       						= [to_numpy(a_						) for v in value]

        # Convert padding_strategy in PaddingStrategy
			lowerCAmelCase  :     Optional[int]       						= self._get_padding_strategies(padding=a_		,   max_length=a_						)

			lowerCAmelCase  :     Dict       						= processed_features[self.model_input_names[0]]

			lowerCAmelCase  :     Any       						= len(a_						)
			if not all(len(a_						) == batch_size for v in processed_features.values()						):
					raise ValueError("Some items in the output dictionary have a different batch size than others."						)

			lowerCAmelCase  :     Tuple       						= []
			for i in range(a_						):
					lowerCAmelCase  :     Any       						= {k: v[i] for k, v in processed_features.items()}
					# truncation
					lowerCAmelCase  :     Tuple       						= self._truncate(
					    a_		,   max_length=a_		,   pad_to_multiple_of=a_		,   truncation=a_		,   )
					truncated_inputs.append(a_						)

			if padding_strategy == PaddingStrategy.LONGEST:
					# make sure that `max_length` cannot be longer than the longest truncated length
					lowerCAmelCase  :     Optional[Any]       						= max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
					lowerCAmelCase  :     int       						= PaddingStrategy.MAX_LENGTH

			lowerCAmelCase  :     List[Any]       						= {}
			for i in range(a_						):
					# padding
					lowerCAmelCase  :     Optional[int]       						= self._pad(
					    truncated_inputs[i]		,   max_length=a_		,   padding_strategy=a_		,   pad_to_multiple_of=a_		,   return_attention_mask=a_		,   )

					for key, value in outputs.items():
							if key not in batch_outputs:
									lowerCAmelCase  :     Dict       						= []
							if value.dtype is np.dtype(np.floataa						):
									lowerCAmelCase  :     int       						= value.astype(np.floataa						)
							batch_outputs[key].append(a_						)

			return BatchFeature(a_		,   tensor_type=a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = PaddingStrategy.DO_NOT_PAD		,   a_ = None		,   a_ = None		,   ):
			lowerCAmelCase  :     Tuple       						= processed_features[self.model_input_names[0]]

			if padding_strategy == PaddingStrategy.LONGEST:
					lowerCAmelCase  :     Dict       						= len(a_						)

			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     int       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     List[Any]       						= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a_						) < max_length

			if return_attention_mask and "attention_mask" not in processed_features:
					lowerCAmelCase  :     Any       						= np.ones(len(a_						)		,   dtype=np.intaa						)

			if needs_to_be_padded:
					lowerCAmelCase  :     Dict       						= max_length - len(a_						)
					if self.padding_side == "right":
							if return_attention_mask:
									lowerCAmelCase  :     int       						= np.pad(
									    processed_features["attention_mask"]		,   (0, difference)						)
							lowerCAmelCase  :     Optional[int]       						= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					elif self.padding_side == "left":
							if return_attention_mask:
									lowerCAmelCase  :     Dict       						= np.pad(
									    processed_features["attention_mask"]		,   (difference, 0)						)
							lowerCAmelCase  :     Any       						= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					else:
							raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

			return processed_features



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			if not truncation:
					return processed_features
			elif truncation and max_length is None:
					raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."						)

			lowerCAmelCase  :     int       						= processed_features[self.model_input_names[0]]

			# find `max_length` that fits `pad_to_multiple_of`
			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     Union[str, Any]       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     Any       						= len(a_						) > max_length

			if needs_to_be_truncated:
					lowerCAmelCase  :     Optional[int]       						= processed_features[self.model_input_names[0]][:max_length]
					if "attention_mask" in processed_features:
							lowerCAmelCase  :     Any       						= processed_features["attention_mask"][:max_length]

			return processed_features





	def 	_lowerCamelCase     ( self		,   a_=False		,   a_=None						):

			# Get padding strategy
			if padding is not False:
					if padding is True:
							lowerCAmelCase  :     str       						= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
					elif not isinstance(a_		,   a_						):
							lowerCAmelCase  :     str       						= PaddingStrategy(a_						)
					elif isinstance(a_		,   a_						):
							lowerCAmelCase  :     Optional[int]       						= padding
			else:
					lowerCAmelCase  :     Tuple       						= PaddingStrategy.DO_NOT_PAD

			# Set max length if needed
			if max_length is None:
					if padding_strategy == PaddingStrategy.MAX_LENGTH:
							raise ValueError(
							    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''						)

        # Test if we have a padding value
			if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
					raise ValueError(
					    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
					    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."						)

			return padding_strategy



